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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

VML 3.0 Reactive Sequencing Objects and Matrix Math Operations for Attitude Profiling

VML (Virtual Machine Language) has been used as the sequencing flight software on over a dozen JPL deep-space missions, most recently flying on GRAIL and JUNO. In conjunction with the NASA SBIR entitled "Reactive Rendezvous and Docking Sequencer", VML version 3.0 has been enhanced to include object-oriented element organization, built-in queuing operations, and sophisticated matrix / vector operations. These improvements allow VML scripts to easily perform much of the work that formerly would have required a great deal of expensive flight software development to realize. Autonomous turning and tracking makes considerable use of new VML features. Profiles generated by flight software are managed using object-oriented VML data constructs executed in discrete time by the VML flight software. VML vector and matrix operations provide the ability to calculate and supply quaternions to the attitude controller flight software which produces torque requests. Using VML-based attitude planning components eliminates flight software development effort, and reduces corresponding costs. In addition, the direct management of the quaternions allows turning and tracking to be tied in with sophisticated high-level VML state machines. These state machines provide autonomous management of spacecraft operations during critical tasks like a hypothetic Mars sample return rendezvous and docking. State machines created for autonomous science observations can also use this sort of attitude planning system, allowing heightened autonomy levels to reduce operations costs. VML state machines cannot be considered merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. The state machine approach enabled by VML 3.0 is progressing toward flight capability with a wide array of applicable mission activities.

VML (Virtual Machine Language)↗
NASA NTRS2022

Adapting a Trusted AI Framework to Space Mission Autonomy

As artificial intelligence (AI) is increasingly pro- posed for new and future capabilities in space missions, the question of how to trust AI-enabled space autonomy has been explored. Recently, a collaboration between The Aerospace Corporation (Aerospace) and NASA’s Jet Propulsion Labora- tory (JPL) investigated how Aerospace’s Trusted AI Frame- work could be applied to two JPL projects that planned on lev- eraging AI for critical autonomous tasks. This combined effort led to many insights in the practical implementation of trusted AI along with considerable updates to the Trusted AI Frame- work that tailored its topic threads to space exploration. This document cohesively summarizes the enhanced framework as tailored to space missions as well as estimation of the level of trust required as a function of mission criticality and key stakeholders. The goal of this work is to provide a set of best practices to inform autonomy researchers, flight engineers, mission and proposal reviewers, and instrument and mission principal investigators (PI’s) to drive AI-based autonomy that maximizes trust and lowers the barriers to mission adoption for both science and engineering applications.

Amini, Rashied↗
NASA NTRSDate not supplied

Creating an Interface to view Multi-Spacecraft Swarm Telemetry

Distributed Spacecraft Systems are a type of multi-spacecraft mission architecture that can not only provide improved resolution, coverage, and availability of existing missions, but also enable missions that would be previously infeasible using traditional approaches. Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems. In previous science swarm missions, the spacecraft involved have not been able to communicate with each other without utilizing a ground station. Now that the spacecraft can perform inter-satellite communication, the spacecraft can be treated as a collective. Swarm autonomy is critical for a growing number of satellites which means novel ways of displaying swarm data needs to be implemented. Such systems introduce unique challenges to traditional approaches for command and control of these spacecraft, due to the large number of spacecraft and the complexity of the interactions between them. The ground data system for DSA addresses these challenges through the creation of a custom user interface that allows a single operator to orchestrate a multi-spacecraft swarm in a scalable way. This plenary describes the details of the autonomy demonstration being performed, the requirements of those using the interface to analyze the spacecraft telemetry to assess demonstration success, and the approach taken by the ground systems team to create an interface that satisfies these requirements. This approach involves the creation of several distinct components that correspond to the level of detail presented to the user. These components are based on conventional user roles in human-robot interaction, including supervisor, operator, and mechanic, extended to accommodate the additional overhead of coordinating actions between agents. One main feature of the interface is the listenability matrix component which will represent inter-satellite communications in a heat mapped matrix. The above work described will enable users to command and interact with the spacecraft as a collective.

human-swarm interaction↗
NASA NTRS2008

Autonomy and Sensor Webs: The Evolution of Mission Operations

Demonstration of these sensor web capabilities will enable fast responding science campaigns that combine spaceborne, airborne, and ground assets. Sensor webs will also require new operations paradigms. These sensor webs will be operated directly by scientists using science goals to control their instruments. We will explore these new operations architectures through a study of existing sensor web prototypes.

Sherwood, Rob↗
NASA NTRS2021

Case Studies in Verifying Spacecraft Autonomy

Spacecraft, operating with a time-to-effect faster than human command response, or with absent or delayed communication, have depended on autonomy. It is likely that this reliance will continue, and increase further in future missions. Spacecraft autonomy is used when operator intervention is not available or feasible, or when dynamic aspects of a spacecraft situation cannot be predicted in advance. This paper provides case studies of several successfully verified autonomous software systems across multiple spacecraft, flight phases (launch, transit, entry and landing, science utilization), and software classifications, and can serve as examples to future missions in methods of successfully assuring spacecraft autonomy.

Johnson, Stephen B.↗
NASA NTRS2000

AI in Space: The Era of Autonomous Space Systems

The development of autonomy capabilities is the key to three vastly important strategic technical challenges facing NASA: the reduction of mission costs, the continuing return of quality science products through limited communications bandwidth, and the launching of a new era of solar system exploration characterized by sustained presence and in-depth scientific studies, including the search for life. Autonomy will benefit future NASA missions by migrating routine, traditionally ground-based functions to the spacecraft, by directly supporting the decoupling of spacecraft from the ground through new operations concepts, by enabling direct links between scientists and the space platforms carrying their instruments of investigation, and by the closing, of planning and control loops onboard, enabling space platforms to directly address uncertainty in the real-time mission context. The talk will survey ongoing, autonomy technology development projects at NASA, many of which have been or will soon be the subject of flight technology experiments, or are already targeted for mission use. The talk will also survey the exciting suite of future NASA space exploration missions, and make the case for the central role of autonomy in achieving the goals of these bold, unprecedented missions: cooperating rovers on the surface of Mars, the search for Earth-like planets around nearby stars, asteroid and comet landers, aerobots in planetary atmospheres, and a series of missions to intriguing Europa, perhaps culminating in a submersible to investigate its putative ocean. Finally, the talk will conclude with some farther-reaching speculations on how to create properties such as long-term survivability and evolvability in future space systems, such that they will be well equipped to extend humanity exploratory presence into the interstellar realm.

Doyle, Richard J.↗
NASA NTRS2003

Autonomous Sciencecraft Experiment (ASE) Test Operations in 2003

NASA has identified the development of an autonomously operating spacecraft as a necessity for an expanded program of missions exploring the Solar System. The Autonomous Sciencecraft Experiment (ASE) has been selected for flight demonstration by NASA s New Millennium Program (NMP) as part of the Space Technology 6 (ST6) mission. ASE is scheduled to fly on the US Air Force Research Laboratory (AFRL) Techsat-21 constellation in 2006. Tech- Sat-21 consists of three satellites flying in a variable-geometry formation in Earth orbit. Each satellite is equipped with X-band Synthetic Aperture Radar, yielding high spatial resolution images (approx. 3 m) of the Earth s surface. The constellation will fly at an altitude of 550 km, in a 35.4 inclination circular orbit, yielding exact repeat-track observations every 13 days. Prior to full deployment, elements of the versatile ASE spacecraft command and control software, image formation software and science processing software will be utilized and tested on two very different platforms in 2003: AirSAR and EO-1 (described below). Advantages of Autonomous Operations: ASE will demonstrate advanced autonomous science data acquisition, processing, and product downlink prioritization, as well as autonomous spacecraft command and control, and fault detection. The advantages of spacecraft autonomy are to future missions include: (a) making the best use of reduced downlink; (b) the overcoming of communication delays through decisionmaking in situ, enabling fast reaction to dynamic events; (c) an increase of science content per byte of returned data; and (d) an avoidance of return of null (no-change/no feature) datasets: if there is no change detectable between two scenes of the same target, there is no need to return the second dataset.

Chien, S.↗
NASA NTRS2005

Current results from a Rover Science Data Analysis System

In this paper, we provide a brief overview of the OASIS system, and then describe our recent successes in integrating with and using rover hardware. OASIS currently works in a closed loop fashion with onboard control software (e.g., navigation and vision) and has the ability to autonomously perform the following sequence of steps: analyze gray scale images to find rocks, extract the properties of the rocks, identify rocks of interest, retask the rover to take additional imagery of the identified target and then allow the rover to continue on its original mission. We also describe the early 2004 ground test validation of specific OASIS components on selected Mars Exploration Rover (MER) images. These components include the rockfinding algorithm, RockIT, and the rock size feature extraction code. Our team also developed the RockIT GUI, an interface that allows users to easily visualize and modify the rock-finder results. This interface has allowed us to conduct preliminary testing and validation of the rockfinder's performance.

Coupled Layer Architecture for Robotic Autonomy (C↗
NASA NTRS2003

Relative benefits of potential autonomy technology investments

We developed a framework that looks at both cost and risk early in the design process in order to determine the investment strategy in new technology development that will lead to the lowest risk mission possible which enables desired science return within a given budget.

autonomy technology prioritization decision analys↗
DOE OSTI2026

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗
NASA NTRS2017

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project

Over the past 5 years, the UAS integration into the NAS project has worked to reduce technical barriers to integration. A major focus of this work has been in support of RTCA SC-228. This committee has recently published the first UAS integration minimum performance standards (MOPS). This work has spanned detect and avoid (DAA) as well as command and control comm datalinks. I will discuss DAA efforts with focus on the human systems work. I will discuss how automation was discussed and addressed within this context. ICAO stood up a remotely piloted aircraft systems (RPAS) panel in 2014. They have developed an RPAS manual and are now working to revise existing annexes and standards and recommended practices. The Human In The System (HITS) has worked to infuse human factors guidelines into those documents. I will discuss that effort as well as how ICAO has defined and address autonomy. There is a great deal of interest in the control of multiple vehicles by a single operator. The UAS EXCOM Science and Research Panel (SARP) is holding a workshop on this topic in late June. I will discuss research performed on this topic when I worked for the Army and on-going work within the division and a NATO working group on Human-Autonomy Teaming.

Unmanned Aircraft Systems (UAS)↗
NASA NTRS2005

The Earth Science Afternoon Constellation: Preparing for Autonomous but Coordinated Operations

This paper describes how the challenges of coordinating the autonomous operations of geographically dispersed mission control centers for several small and large satellites are being overcome. The Earth Science Afternoon Constellation, also referred to as the "A-Train", is an international grouping of five NASA satellites (two major NASA EOS missions and three NASA/Earth System Science Pathfinder missions) and one French satellite orbiting in close proximity. This grouping of satellites provides scientists with the opportunity to perform coincident observations using data from two or more instruments on various satellites with measurements taken at approximately the same time. Three of the six missions are currently on-orbit, with the two missions expected to join the constellation later this year and one mission in 2007. The operational challenges are daunting for several reasons. There are several Mission Control Centers (widely separated on two continents), operating autonomously under tight budget constraints. All of the Mission Control Centers have reasons to be concerned about safety while flying in close proximity to other satellites, but most Centers did not have the resources or the desire to address this concern alone - the interfaces are too numerous and anticipated operations too costly. Clearly, an efficient approach was needed. This paper describes the steps taken to make this Earth science constellation a reality. Agreements were forged to allow the Mission Control Centers to maintain their autonomy, while ensuring their satellite's safety. Each member mission in the constellation operates independently in accordance with its own mission requirements, but the member missions have agreed to coordinate their operations, i.e., orbital positions and control to ensure the safety of the entire constellation. A centralized system was developed at NASA Goddard Space Flight Center to collect, analyze, and distribute ephemeris data used by each of the mission teams to determine the positions of the satellites in the constellation. The system issues warnings regarding possible dangerous configurations, eliminating the need for redundant capabilities at each Mission Control Center. On-orbit contingency situations were identified and analyzed; agreements were reached in advance of contingency operations to ensure that coordination between the Mission Control Centers can be handled expeditiously and fairly. In this manner, recovery from anomalous situations can be more quickly realized, thereby increasing the science return and reducing costs. The process used to develop these contingency procedures and the systems used to facilitate the contingency resolution are described as well.

Case, Warren↗
NASA NTRS2002

Challenges in mobility and robotics for in-situ science

In-situ science on planetary surfaces such as Mars, Venus, Mercury and Titan pose extreme challenges for mobile robots. Future missions will involve surface, subsurface, and atmospheric mobility which focuses the need for technology development in sensing, autonomy, and mobile robot architectures for solar system exploration.

challenges mobility robotics in-situ science↗
NASA NTRS2004

Mars Exploration Rover Mission

Two rovers with a sophisticated geological payload have been operating on the surface of Mars since January of 2004. Future missions and their related technology developments will benefit from the lessons learned during these surface operations. The planning cycle was dictated by the communications opportunities and the times of day that the rovers could operate, and the team and tools were tuned to optimize the mission return for that cycle time. The ability to traverse and to approach and perform in situ investigations on targets was limited in speed by the same cycle time, due to required human involvement in the related planning and risk decisions. In addition traverse was limited by the speed of the on-board terrain and hazard assessment, and in situ operations were limited by a lack of autonomy. Different planning cycles and levels of autonomy should be considered for future surface missions, which will result in different approaches to science decision making.

Adler, M.↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
NASA NTRS2005

Autonomy Software Architecture for LORAX (Life On ice Robotic Antarctic eXplorer)

LORAX is a robotic astrobiological study of the ice field surrounding the Carapace Nunatak near the Allan Hills in Antarctica. The study culminates in a l00km traverse, sampling the ice at various depths (from surface to 10cm) at over 100 sites to survey microbial ecology and to record environmental parameters. The autonomy requirements from LORAX are shared by many robotic exploration tasks. Consequently, the LORAX autonomy architecture is a general architecture for on-board planning and execution in environments where science return is to be maximized against resource limitations and other constraints.

Jonsson, Ari↗